While recent neural dialogue models have explicitly modelled dialogue state through belief and user intent trackers  [1]}, [2]}, [3]}, we choose instead to rely on learned neural representations for implicit modelling of dialogue state, forming a truly end-to-end trainable system. Our model starts with an encoder-decoder sequence architecture and is further augmented with an attention-based retrieval mechanism that effectively reasons over a key-value representation of the underlying knowledge base. We describe each component of our model in the subsequent sections.
